Package {salmonMSE}

Title:	Management Strategy Evaluation for Salmon Species
Version:	3.0.0
Date:	2026-08-18
Maintainer:	Quang Huynh <quang@bluematterscience.com>
Description:	Simulation tools to evaluate the long-term effects of salmon management strategies, including a combination of habitat, harvest, and habitat actions. The stochastic age-structured operating model accommodates complex life histories, including freshwater survival across early life stages, juvenile survival and fishery exploitation in the marine life stage, partial maturity by age class, and fitness impacts of hatchery programs on natural spawning populations. 'salmonMSE' also provides an age-structured conditioning model to develop operating models fitted to data.
License:	GPL (≥ 3)
Depends:	R (≥ 4.1.0)
Imports:	abind, RTMB (≥ 1.9), dplyr, ggplot2, grDevices, gsl, methods, reshape2, rlang, rmarkdown, parallel, stats, utils
Suggests:	bookdown, EnvStats, ggrepel, knitr, rstan, scales, testthat (≥ 3.0.0), tmbstan
Encoding:	UTF-8
LazyData:	yes
LazyLoad:	yes
RoxygenNote:	7.3.3
URL:	https://docs.salmonmse.com/, https://github.com/Blue-Matter/salmonMSE
BugReports:	https://github.com/Blue-Matter/salmonMSE/issues
NeedsCompilation:	no
Packaged:	2026-08-19 23:29:45 UTC; quang
Author:	Quang Huynh [aut, cre]
Repository:	CRAN
Date/Publication:	2026-08-20 10:00:02 UTC

Calculate exploitation rate in conditioning model

Description

Internal functions for calculating aggregate exploitation rate (.CM_ER()) or by age class (.CM_ER()).

Usage

.CM_ER(
  report,
  type = c("PT", "T", "all"),
  r = 1,
  index_AEQ = NULL,
  brood = FALSE,
  simplify = TRUE
)

.CM_ERage(report, type = c("PT", "T"), brood = FALSE, simplify = TRUE)

Arguments

Value

An array if simplify = TRUE. Otherwise, a list.

Reference points for conditioning model

Description

Internal functions that calculate productivity (.CM_prod()) and reference points (.CM_MSY()) from the conditioning model. These functions can be used to calculate values for a subset of years (productivity can vary in time with natural mortality and maturity), or from average biological parameters in a subset of years.

Usage

.CM_prod(report, d, index = NULL, mean_bio = FALSE)

.CM_Srep(report, d, index = NULL, mean_bio = FALSE, type = c("spawner", "egg"))

.CM_MSY(
  report,
  d,
  simple = TRUE,
  index = NULL,
  mean_bio = FALSE,
  type = c("spawner", "egg", "u", "Sgen"),
  AEQ = TRUE,
  maximize = c("MSY", "MER"),
  ncores = 1
)

Arguments

Value

Matrix, dimension ⁠[length(index), length(report)]⁠. If mean_bio = TRUE, matrix has 1 row.

See Also

CM_MSY()

Egg production function

Description

Simple wrapper function to calculate hatchery egg production

Usage

.egg_func(
  ptake = 1,
  N,
  gamma = 1,
  fec,
  p_female,
  s_prespawn,
  val = 0,
  opt = TRUE
)

Arguments

Value

Numeric

All-H Analyzer

Description

Wrapper function for an implementation of All-H Analyzer (AHA) in R. Can be used to compare outputs between AHA and salmonMSE.

Usage

AHA(SOM, ngen = 100, silent = FALSE)

Arguments

Value

A named list containing vectors of state variables (by simulation, population, and generation). See SMSE object description.

References

Hatchery Scientific Review Group. 2020. All-H Analyzer Tool Guide and Documentation. May 2020.

Class "Bio"

Description

The component of the operating model that controls biological dynamics, i.e., natural production.

Details

Various parameters can be stochastic (length nsim) or input as a single numeric (value identical across all simulations).

Slots

Name

Character. Identifying name

maxage

Integer. The maximum age of the population age structure.

n_g

Integer. Number of life history groups within a cohort. Life history groups (LHGs) are sub-units of a cohort that have different marine survival. Default is 1.

p_LHG

The proportion of the total egg production assigned to each life history group within a cohort. For example, if Bio@n_g <- 2, then Bio@p_LHG <- c(0.9, 0.1), then 90 percent of the egg production in the first population is assigned to the first life history group and ten percent to the second LHG. Default is rep(1/Bio@n_g, Bio@n_g)

p_mature

Either vector by age (length maxage) or an array with dimension ⁠[nsim, maxage, proyears]⁠. The proportion mature by age.

SRrel

Character, stock-recruit relationship for density-dependent smolt production from fry. Either "BH" (Beverton-Holt) or "Ricker". Not used if habitat component is used. See Habitat object.

capacity

Vector length nsim. Only used if SRrel = "BH". The asymptote, i.e., maximum juvenile production, of the Beverton-Holt stock-recruit function for density-dependent natural smolt production from egg production. Units of smolts. Not used if habitat component is used.

kappa

Vector length nsim. The adult productivity ratio for the stock-recruit function. Units of recruits per spawner. Natural per-capita production of recruits as the population approaches zero (density-independent component). Not used if habitat component is used.

Smax

Vector length nsim. Only used if SRrel = "Ricker". The spawner abundance that maximizes smolt production in the Ricker stock-recruit function. Units of spawners. Not used if habitat component is used.

phi

Optional, vector length nsim. Egg production per smolt at unfished replacement. Units of egg per smolt. Converts productivity from units of recruits/spawner (kappa) to smolts/egg (alpha), where alpha = kappa/phi. In simple models, phi is the product of marine survival, fecundity, and proportion female. If not provided, phi will be calculated from Mjuv_NOS, p_mature, s_enroute, p_female, fec, and p_LHG corresponding to the first year and weighted by life history groups. Not used if habitat component is used.

tau

Optional, vector length nsim. Spawner per smolt at unfished replacement, only used if SRrel = "Ricker". Units of spawner per smolt. Used to convert Smax to Emax, the corresponding egg production that maximizes smolt production, where Emax = Smax * phi/tau and the Ricker parameter beta = 1/Emax. In simple models, tau is the product of marine survival and proportion female. If not provided, tau will be calculated from Mjuv_NOS, p_mature, s_enroute, p_female, and p_LHG corresponding to the first year and weighted by life history groups. Not used if habitat component is used.

Mjuv_NOS

Either vector by age (length maxage-1) or an array with dimension ⁠[nsim, maxage-1, proyears, n_g]⁠. Natural mortality of immature natural origin fish, the value for the first age represents natural mortality from age 1 to 2, second age is mortality from age 2 to 3, and so on. To replicate the SAR parameter of a stage-specific model, set Mjuv_NOS[a] = -log(SAR) where a is the age class prior to maturation (and zero for all other ages).

fec

Vector by age (length maxage) or an array with dimension ⁠[nsim, maxage, proyears]⁠. Female fecundity of natural origin spawners.

p_female

Numeric. The proportion of females in the spawning population. Default is 0.5. Can also be a vector ⁠[maxage]⁠ (for situations where older spawners are predominantly female)

s_enroute

Numeric. Survival of escapement to the spawning grounds (for spawning and for broodtake). Default is 1.

Creating Object

Objects can be created by calls of the form new("Bio")

Examples

showClass("Bio")

Convert conditioning model to operating model

Description

Creates an operating model from MCMC samples and data inputs of the conditioning model. Management actions for habitat, hatchery production, and harvest still need to be specified in the operating model.

Note: the function assumes the maturity values in the last conditioning year for the projection, which are likely not well informed by CWT data. Consider updating the maturity using some historical average (e.g., across most recent completed brood years).

Usage

CM2SOM(stanfit, sims, nsim = 2, seed = 1, proyears = 40)

Arguments

Value

SOM object.

Plot reference points from conditioning model

Description

Plots time series of MSY reference points. Not used in automated reporting as they can be computationally expensive and individual case studies may require specific assumptions.

Usage

CM_MSY(
  report,
  d,
  year1 = 1,
  simple = FALSE,
  index = NULL,
  mean_bio = FALSE,
  type = c("spawner", "egg", "u"),
  maximize = c("MSY", "MER"),
  AEQ = TRUE,
  ncores = 1,
  na.rm = FALSE
)

CM_Sgen(
  report,
  d,
  year1 = 1,
  simple = FALSE,
  index = NULL,
  mean_bio = FALSE,
  ncores = 1,
  na.rm = FALSE
)

Arguments

Value

ggplot object

See Also

CM_prod() CM_Srep() .CM_MSY()

Estimation function for conditioning model

Description

Population dynamics model of an age structured salmon population. Used with RTMB to estimate historical reconstruction from data.

Usage

CM_int(p, d)

Arguments

Value

Numeric, objective function value (log-posterior)

Author(s)

Q. Huynh with Stan code provided by J. Korman and C. Walters

Deprecated functions

Description

Deprecated performance metric functions. These functions are now replaced with functions of same name but prefixed with P_.

Usage

PNI50(...)

PNI80(...)

WILD50(...)

SMSY85(...)

Sgen100(...)

Arguments

Value

A vector of probabilities

See Also

P_PNI50()

Solve for fishing effort

Description

Internal solver used by catch_func() to calculate the fishing effort needed to achieve target harvest rate or catch rate, subject to partial retention due to mark-selective fishing. Harvest rate is discounted by adult equivalents for preterminal fisheries.

Usage

Effort_solver(
  Eff,
  N,
  vul,
  ret,
  release_mort,
  type = c("u", "catch"),
  u = 0,
  K = 0,
  AEQ = array(1, dim(N)),
  p_mature = array(1, dim(N))
)

Arguments

Value

Numeric. Returns the difference between the realized harvest rate (for the given value of Eff) and the target

Class "Habitat"

Description

The component of the operating model that controls survival in the freshwater environment. Includes changes in survival from either environmental/climate effects or habitat mitigation.

Slots

Name

Character. Identifying name

use_habitat

Logical. If TRUE, utilize stage-specific density-dependent functions from egg production from incubation mortality, egg-to-fry production, and fry-to-smolt production with annual deviations. Otherwise, the density-dependence is modeled for egg-to-smolt survival. See Bio object.

prespawn_rel

Character, density-dependent function for pre-spawn mortality, e.g., for spawners to reach spawning sites. Choices are "BH" (Beverton-Holt) or "HS" (hockey stick). Default is "BH".

prespawn_prod

Numeric, productivity for pre-spawn mortality. Default is 1. Default if Inf.

prespawn_capacity

Numeric, capacity for pre-spawn mortality. Default is Inf, i.e., density-independence. Default is Inf.

egg_rel

Character, density-dependent function for egg production from total spawning output. Choices are "BH" (Beverton-Holt) or "HS" (hockey stick). Default is "BH".

egg_prod

Numeric, productivity for egg production from total spawning output (incubation). Default is 1. Default if Inf.

egg_capacity

Numeric, capacity for egg production from total spawning output (incubation). Default is Inf, i.e., density-independence. Default is Inf.

fry_rel

Character, density-dependent function for egg-to-fry production. Choices are "BH" (Beverton-Holt) or "HS" (hockey stick). Default is "BH".

fry_prod

Numeric between 0-1, productivity for egg production from total spawning output, i.e., maximum survival as egg production approaches zero. Default is 0.4.

fry_capacity

Numeric, capacity for fry production from egg production. Default is Inf, i.e., for density-independence. Default is Inf.

fry_sdev

Matrix ⁠[nsim, proyears]⁠, deviations from the density-dependent egg-fry survival. Can be utilized to incorporate time-varying environmental, climate, or habitat mitigation effects. Default is matrix(1, nsim, proyears).

smolt_rel

Character, density-dependent function for fry-to-smolt production. Choices are "BH" (Beverton-Holt) or "HS" (hockey stick). Default is "BH".

smolt_prod

Numeric between 0-1, productivity for smolt production from fry, i.e., maximum survival as fry production approaches zero. Default is 1.

smolt_capacity

Numeric, capacity for smolt production from fry production. Set to Inf for density-independence. Default is Inf.

smolt_sdev

Matrix ⁠[nsim, proyears]⁠, deviations from the density-dependent fry-smolt survival. Can be utilized to incorporate time-varying environmental, climate, or habitat mitigation effects. Default is matrix(1, nsim, proyears).

Creating Object

Objects can be created by calls of the form new("Habitat")

Examples

showClass("Habitat")

Class "Harvest"

Description

The component of the operating model that controls marine harvest.

Slots

Name

Character. Identifying name

type_PT

Character. Whether to manage preterminal fishery catch from exploitation rate ("u") or catch target ("catch"). Default is "u".

type_T

Character. Whether to manage terminal fishery catch from exploitation rate ("u") or catch target ("catch"). Default is "u".

u_preterminal

Numeric, matrix ⁠[nsim, proyears]⁠, or function. If type_PT = "u", the harvest rate of the immature component of the population in the pre-terminal fishery. The harvest rate is the ratio to kept AEQ catch to (kept AEQ catch + return), where AEQ are adult equivalents. Function should be of the form function(NO, HO, m) return(u).

u_terminal

Numeric, matrix ⁠[nsim, proyears]⁠, or function. If type_T = "u", the harvest rate (ratio of kept catch to of the terminal marine fishery. Function should be of the form function(NO, HO, m) return(u).

K_PT

Numeric or function. If type_PT = "catch", the catch target of the immature component of the population in the pre-terminal fishery. Function should be of the form function(NO, HO, m) return(K).

K_T

Numeric or function. If type_T = "catch", the catch target of the return in the terminal fishery. Function should be of the form function(NO, HO, m) return(K).

MSF_PT

Logical. Whether to implement mark-selective fishing in the preterminal fishery, with no retention on unmarked fish.

MSF_T

Logical. Whether to implement mark-selective fishing in the terminal fishery, with no retention on unmarked fish.

release_mort

Vector length 2. The proportion of released fish that die after release, in the pre-terminal and terminal fishery. Implemented to model mark-selective fishing. Not used if either MSF_PT or MSF_T is FALSE.

vulPT

Vector length maxage or matrix ⁠[nsim, maxage]⁠. Vulnerability schedule (between 0-1) in the preterminal fishery. Values indicate the proportion of fishing mortality experienced by each age class, where F_preterminal = -log(1 - u_preterminal).

vulT

Vector length maxage or matrix ⁠[nsim, maxage]⁠. Vulnerability schedule (between 0-1) in the terminal fishery. Values indicate the proportion of fishing mortality experienced by each age class, where F_terminal = -log(1 - u_terminal).

Creating Object

Objects can be created by calls of the form new("Harvest")

Examples

showClass("Harvest")

Class "Hatchery"

Description

The component of the operating model that controls the hatchery management and in-river removals.

Details

Various parameters can be stochastic (length nsim) or input as a single numeric (value identical across all simulations).

A description of the fitness parameters and hatchery dynamics is available in the online documentation.

Slots

Name

Character. Identifying name

n_r

Integer. Number of release strategies, sub-groups of fish with different survival schedules. Default is one.

n_yearling

Vector length n_r. The target number of yearlings released by release strategy. No hatchery is modeled if sum(n_yearling, n_subyearling) = 0. are zero. Default is zero. Yearlings are differentiated from sub-yearlings only in the hatchery survival parameter, e.g., lower egg-yearling survival than for egg-subyearling, so more brood is needed for yearling releases.

n_subyearling

Vector length n_r. The target number of subyearlings released. No hatchery is modeled if sum(n_yearling, n_subyearling) = 0 are zero. Default is zero. Yearlings are differentiated from sub-yearlings only in the hatchery survival parameter, e.g., lower egg-yearling survival than for egg-subyearling, so more brood is needed for yearling releases.

yearling_DD

Logical, whether freshwater yearling survival after release is density-dependent, in competition with natural-origin juveniles. Default is FALSE. Likely relevant only for stream types where there is substantial residence time before outmigration.

subyearling_DD

Logical, whether freshwater subyearling survival after release is density-dependent, in competition with natural-origin juveniles. Default is FALSE. Likely relevant only for stream types where there is substantial residence time before outmigration.

s_prespawn

Numeric. The survival of broodtake prior to egg production. 1 - s_prespawn is the proportion of fish not used for hatchery purposes, e.g., mortality or other resesarch purposes. Used to back-calculate the broodtake from n_yearling and n_subyearling. Default is 1.

s_egg_smolt

Numeric. The survival of eggs to the smolt life stage (for yearling release). Used to back-calculate the broodtake from n_yearling and n_subyearling. Default is 1.

s_egg_subyearling

Numeric. The survival of eggs to subyearling life stage (for subyearling release). Used to back-calculate the broodtake from n_yearling and n_subyearling. Default is 1.

Mjuv_HOS

Either vector by age (length maxage-1) or an array with dimension ⁠[nsim, maxage-1, proyears, n_r]⁠. Natural mortality of immature hatchery origin fish. To replicate the SAR parameter of a stage-specific model, set Mjuv_HOS[a] = -log(SAR) for the age class prior to maturation (and zero for all other ages).

p_mature_HOS

Vector by age (length maxage) or an array with dimension ⁠[nsim, maxage, proyears, n_r]⁠ for the maturity of hatchery spawners. Default is set equal to Bio@p_mature for all release strategies.

stray_external

Matrix by age (length maxage) and release strategy n_r that denotes the annual number of hatchery origin strays from other populations/systems not included in the operating model. Default is zero. External strays are added at the escapement life stage, assumed unmarked. For multi-population models with straying within the system, see also SOM@stray matrix.

gamma

Numeric. The relative reproductive success of hatchery origin spawners (relative to natural origin spawners). Default is 1.

m

Numeric. The mark rate of hatchery origin fish, which affects selective broodtake and fishery retention if mark-selective fishing is utilized. Set m = 1 for AHA compatibility with ptarget_NOB. Default is zero.

f_brood

Function that calculates the natural origin brood and hatchery origin brood from the escapement (after en-route mortality). Function should be of the form function(NO, HO, stray, m) {return(list(NOB, HOB_marked, HOB_unmarked, HOB_stray))}. Allows for bespoke rules for broodtake.

brood_import

Not used if f_brood is provided Matrix by age (length maxage) for the number of annual imported hatchery origin broodstock. Egg production is weighted by fec_brood. To meet hatchery production target releases, imported brood and local marked brood are used indiscriminately. Default imported brood is zero.

pmax_esc

Not used if f_brood is provided Numeric. The maximum proportion of total escapement (after en route mortality) that could be used as broodtake. Set to 1 for AHA compatibility. Default is 0.75.

pmax_NOB

Not used if f_brood is provided Numeric. The maximum proportion of the natural origin escapement (after en route mortality and pmax_esc) to be used as broodtake. If broodstock is limited by pmax_esc < 1, then this parameter should be 1. Default is 1.

ptarget_NOB

Not used if f_brood is provided Numeric. The target proportion of the natural origin broodtake relative to the overall broodtake, assuming the mark rate is 1 and natural origin fish can be identified in the hatchery. The realized proportion may be lower if there are insufficient natural origin escapement. If the mark rate < 1, then this target proportion identifies the proportion of unmarked fished in the broodtake. If mark rate = 0, then pNOB is equal to the proportion in the escapement. Default is 0.9.

phatchery

Not used if f_brood is provided Numeric. Optional parameter (default is NA). If set to a numeric between 0-1, this value is the proportion of the hatchery origin escapement that return to the hatchery, for example, by removal from spawning grounds or swim-in facilities. These fish are available for broodtake. None of these fish will spawn in the natural environment. With the default option, NA allows all hatchery origin escapement to be available for brood (the remainder go to the spawning grounds).

premove_HOS

Numeric or function. The target proportion of the hatchery origin fish to be removed from the spawning grounds (in order to ensure a high proportion of NOS). The proportion of hatchery fish removed is discounted by the mark rate, i.e., p = premove_HOS * m. The removed hatchery-origin fish do not spawn and are not available for broodtake. A value less than one can represent imperfect implementation of weir removal. Default is zero. This slot can take a function that returns the proportion (p) based on hatchery-origin and natural-origin abundance (after brood removal), thus allowing for bespoke rules for in-river harvest. The function should be of the form: function(NO, HO, m) {return(p)}.

premove_NOS

Numeric or function. The target proportion of the natural origin fish to be removed from the spawning grounds, for example, through an in-river fishery. The proportion of natural fish removed is adjusted by the mark rate, i.e., p = premove_NOS * (1-m). Default is zero. This slot can take a function that returns the proportion (p) based on hatchery-origin and natural-origin abundance (after brood removal), thus allowing for bespoke rules for in-river harvest. The function should be of the form: function(NO, HO, m) {return(p)}.

fec_brood

Vector length maxage or an array with dimension ⁠[nsim, maxage, proyears]⁠. The fecundity schedule of broodtake to calculate the total egg production for the hatchery. If missing, uses Bio@fec.

p_female_brood

Numeric or vector maxage. Proportion of females of brood used to calculate hatchery egg production, for situations with sex-selective brood. If missing, uses Bio@p_female.

fitness_type

Character vector length 2. The fitness function to apply in the natural and hatchery environment, respectively. For each, either "Ford" or "none".

theta

Vector length 2. The optimum phenotype value for the natural and hatchery environments.

rel_loss

Vector length 3. The loss in fitness apportioned among the egg, fry, and smolt life stages which reduces survival. Theoretically, the three values should sum to 1. Alternatively, set to zero to set fitness loss to zero for that specific life stage (survival is one).

zbar_start

Vector length 2. The mean phenotype value in the natural and hatchery populations at the start of the projection. Alternatively, an array by dimension ⁠[nsim, maxage, 2]⁠, where the age slot corresponds to cohort.

fitness_variance

Numeric. The variance (omega-squared) of the fitness function. Assumed identical between the natural and hatchery environments. Default is 100.

phenotype_variance

Numeric. The variance (sigma-squared) of the phenotypic trait (theta). Assumed identical between the natural and hatchery environments. Default is 10.

heritability

Numeric or vector length ⁠[nsim]⁠. The heritability (h-squared) of the phenotypic trait. Between 0-1. Default is 0.5

fitness_floor

Numeric. The minimum fitness value in the natural and hatchery environments, i.e., fitness cannot drop below this threshold. Default is 0.5.

Creating Object

Objects can be created by calls of the form new("Hatchery")

Examples

showClass("Hatchery")

Class "Historical"

Description

Optional component of the operating model that specifies the historical dynamics. The default option starts the projection with 1000 natural-origin juveniles and 1000 hatchery-origin juveniles (if there is hatchery production) in the oldest age class, representing single-brood year returns since there is only one age class in the population. Specify the abundance in all age class to simulate multiple brood-year returns.

Slots

Name

Character. Identifying name

InitNjuv_NOS

Array by ⁠[nsim, maxage, n_g]⁠. The abundance of immature natural origin fish at the beginning of the projection. Default assumes 1000 in the oldest age class, which creates a population with single brood year returns.

InitNjuv_HOS

Array by ⁠[nsim, maxage, n_r]⁠. The abundance of immature hatchery origin fish at the beginning of the projection. Default assumes 1000 in the oldest age class, which creates a population with single brood year returns.

Creating Object

Objects can be created by calls of the form new("Historical")

Examples

showClass("Historical")

Example performance metrics

Description

Functions that evaluate return probabilities of outcomes from the simulations.

Usage

P_PNI50(SMSE, Ref = 0.5, Yrs = NULL)

P_PNI80(SMSE, Ref = 0.8, Yrs = NULL)

P_WILD50(SMSE, Ref = 0.5, Yrs = NULL)

P_SMSY85(SMSE, Ref = 0.85, Yrs = NULL)

P_SMSY80(SMSE, Ref = 0.85, Yrs = NULL)

P_Sgen100(SMSE, Ref = 1, Yrs = NULL)

Arguments

Details

• P_PNI50 calculates the probability that PNI exceeds 0.50 (threshold for an integrated-transition population, Withler et al. 2018)

• P_PNI80 calculates the probability that PNI exceeds 0.80 (threshold for an integrated-wild population, Withler et al. 2018)

• P_WILD50 calculates the probability that at least 50 percent of natural spawners are wild

• P_SMSY80 calculates the probability that NOS exceeds 0.80 SMSY

• P_SMSY85 calculates the probability that NOS/SMSY exceeds 0.85 SMSY

• P_Sgen100 calculates the probability that NOS exceeds Sgen

Value

A vector of probabilities corresponding to population

References

Withler et al. 2018. Genetically Based Targets for Enhanced Contributions to Canadian Pacific Chinook Salmon Populations. DFO Can. Sci. Advis. Sec. Res. Doc. 2018/019. xii + 88 p.

Class "SMSE"

Description

Stores the outputs from the simulation of salmon operating models.

Details

In generation t, proportionate natural influence (PNI) is defined as:

\textrm{PNI}_t = \dfrac{p^\textrm{NOB}_t}{p^\textrm{NOB}_t + p^\textrm{HOSeff}_t}

with p^\textrm{HOSeff} = \textrm{HOSeff}/(\textrm{NOS} + \textrm{HOSeff}).

The proportion of wild salmon is defined as:

p^{\textrm{WILD}}_t = q^\textrm{HOScen}_t \dfrac{(q^\textrm{HOScen}_{t-1})^2} {(q^\textrm{HOScen}_{t-1})^2 + 2\gamma \times p^\textrm{HOScen}_{t-1} q^\textrm{HOScen}_{t-1} + \gamma^2 (p^\textrm{HOScen}_{t-1})^2}

where q = 1-p and p^\textrm{HOScen} = \textrm{HOS}/(\textrm{NOS} + \textrm{HOS}).

Slots

Name

Character. Identifying name

proyears

Integer. The number of projected years

nsim

Integer. The number of simulations

nstocks

Integer. The number of stocks

Snames

Character. Stock names

Egg_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Spawning output, i.e., egg production, of natural origin spawners.

Egg_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Spawning output of hatchery origin spawners.

Fry_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Fry that are offspring of natural origin spawners.

Fry_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Fry that are offspring of hatchery origin spawners.

Smolt_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Smolts that are offspring of natural origin spawners.

Smolt_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Smolts that are offspring of hatchery origin spawners.

Smolt_Rel

Array ⁠[nsim, nstocks, proyears]⁠. Smolts that are offspring of broodtake, i.e., hatchery releases.

Njuv_NOS

Array ⁠[nsim, nstocks, nage, proyears]⁠. Abundance of juvenile natural origin fish at the beginning of the year.

Njuv_HOS

Array ⁠[nsim, nstocks, nage, proyears]⁠. Abundance of juvenile hatchery origin fish at the beginning of the year.

Return_NOS

Array ⁠[nsim, nstocks, nage, proyears]⁠. Mature fish that will be natural origin spawners.

Return_HOS

Array ⁠[nsim, nstocks, nage, proyears]⁠. Mature fish that will be hatchery origin spawners.

Escapement_NOS

Array ⁠[nsim, nstocks, nage, proyears]⁠. The escapement of mature fish that will be natural origin spawners.

Escapement_HOS

Array ⁠[nsim, nstocks, nage, proyears]⁠. The escapement of mature fish that will be hatchery origin spawners.

NOB

Array ⁠[nsim, nstocks, nage, proyears]⁠. Natural origin broodtake.

HOB

Array ⁠[nsim, nstocks, nage, proyears]⁠. Hatchery origin broodtake (local + strays).

HOB_stray

Array ⁠[nsim, nstocks, nage, proyears]⁠. Hatchery origin broodtake (strays only).

HOB_import

Array ⁠[nsim, nstocks, nage, proyears]⁠. Imported hatchery origin broodtake used for hatchery production.

NOS

Array ⁠[nsim, nstocks, nage, proyears]⁠. Natural origin spawners.

HOS

Array ⁠[nsim, nstocks, nage, proyears]⁠. Hatchery origin spawners (local + strays).

HOS_stray

Array ⁠[nsim, nstocks, nage, proyears]⁠. Hatchery origin spawners (strays only).

HOS_effective

Array ⁠[nsim, nstocks, nage, proyears]⁠. Hatchery origin spawners (local + strays) discounted by gamma.

KPT_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Pre-terminal fishery kept catch of natural origin spawners.

KT_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Terminal fishery kept catch of natural origin spawners.

KPT_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Pre-terminal fishery kept catch of hatchery origin spawners.

KT_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Terminal fishery kept catch of hatchery origin spawners.

DPT_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Pre-terminal fishery released catch (live and dead) of natural origin spawners.

DT_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Terminal fishery released catch (live and dead) of natural origin spawners.

DPT_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Pre-terminal fishery released catch (live and dead) of hatchery origin spawners.

DT_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Terminal fishery released catch (live and dead) hatchery origin spawners.

UPT_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Pre-terminal fishery harvest rate (from adult equivalent kept catch) of natural origin spawners.

UT_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Terminal fishery harvest rate of natural origin spawners.

UPT_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Pre-terminal fishery harvest rate of hatchery origin spawners.

UT_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Terminal fishery harvest rate of hatchery origin spawners.

ExPT_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Pre-terminal fishery exploitation rate (from kept catch and dead releases in adult equivalents) of natural origin spawners.

ExT_NOS

Array ⁠[nsim, nstocks, proyears]⁠. Terminal fishery exploitation rate of natural origin spawners.

ExPT_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Pre-terminal fishery exploitation rate of hatchery origin spawners.

ExT_HOS

Array ⁠[nsim, nstocks, proyears]⁠. Terminal fishery exploitation rate of hatchery origin spawners.

fitness

Array ⁠[nsim, nstocks, 2, proyears]⁠. Fitness of the population in the natural (1) and hatchery (2) environments.

pNOB

Array ⁠[nsim, nstocks, proyears]⁠. Proportion of natural fish in the brood.

pHOS_census

Array ⁠[nsim, nstocks, proyears]⁠. Proportion of spawners of hatchery origin, weighted by age class fecundity.

pHOS_effective

Array ⁠[nsim, nstocks, proyears]⁠. Proportion of spawners of hatchery origin, discounted by gamma, weighted by age class fecundity.

PNI

Array ⁠[nsim, nstocks, proyears]⁠. Proportionate natural influence, index of gene flow from hatchery to the natural environment.

p_wild

Array ⁠[nsim, nstocks, proyears]⁠. Proportion of wild spawners, natural spawners whose parents were also produced in the natural environment assuming non-assortative mating, defined under Canada's Wild Salmon Policy.

Mjuv_loss

Array ⁠[nsim, nstocks, nage-1, proyears]⁠. Realized juvenile natural mortality, which may differ from inputs due to fitness loss.

Misc

List. Miscellaneous output:

• Ref for reference points

• SOM for the SOM object (updated by check_SOM()).

• LHG list nstocks long containing state variables by natural-origin life history group

• RS list nstocks long containing state variables by hatchery-origin release strategy

Creating Object

Objects can be created by calls of the form new("SMSE")

References

Withler et al. 2018. Genetically Based Targets for Enhanced Contributions to Canadian Pacific Chinook Salmon Populations. DFO Can. Sci. Advis. Sec. Res. Doc. 2018/019. xii + 88 p.

Examples

showClass("SMSE")

Class "SOM"

Description

An object containing all the parameters for a salmon operating model (SOM).

Slots

Name

Character. Identifying name

nsim

Integer. Number of simulations

proyears

Integer. The number of projected years

seed

Integer. A random seed to ensure users can reproduce results exactly

Bio

Bio object informing biological parameters and natural production. Provide a list of Bio objects for multi-population models.

Habitat

Habitat object containing management levers for controlling survival in the freshwater environment. Provide a list of Habitat objects for multi-population models.

Hatchery

Hatchery object containing management levers for hatchery production and in-river removals. Provide a list of Hatchery objects for multi-population models.

Harvest

Harvest object containing management levers for marine harvest. Provide a list of Harvest objects for multi-population models.

Historical

Historical object to inform historical reconstruction and informing starting abundance for the projection. Provide a list of Historical objects for multi-population models.

stray

Matrix ⁠[np, np]⁠ where np = length(Bio) and row p indicates the re-assignment of hatchery fish to each population when they mature (at the recruitment life stage). For example, SOM@stray <- matrix(c(0.75, 0.25, 0.25, 0.75), 2, 2) indicates that 75 percent of mature fish return to their natal river and 25 percent stray in both populations. By default, an identity matrix is used (no straying).

Objects from the Class

Objects can be created by calls of the form new("SOM", Bio, Habitat, Hatchery, Harvest, Historical).

Brood function

Description

Calculates broodtake and in-river removals from escapement of marine fisheries. This function also applies en-route mortality.

Usage

brood_func(
  Nage_NOS,
  Nage_HOS,
  stray_external,
  m,
  m_stray,
  s_enroute,
  hatchery_args
)

Arguments

Value

Named list:

• broodtake list returned by calc_broodtake()

• hatchery_production list returned by calc_yearling()

• spawners list returned by calc_spawners()

See Also

calc_broodtake()

Calculate abundance from density-dependent mortality

Description

Calculates the abundance of survivors after applying either a Beverton-Holt or Ricker stock-recruit relationship.

Usage

calc_SRR(N1, N2 = N1, p, capacity, type = c("BH", "Ricker", "HS"))

Arguments

Details

The Beverton-Holt stock recruit relationship is of the following form:

\textrm{Smolt} = \dfrac{\alpha N_1}{1 + \beta N_2}

where \alpha = P, \beta = P/C.

The Ricker stock recruit relationship is of the following form:

\textrm{Smolt} = \alpha N_1 \exp(-\beta N_2)

where \alpha = P, \beta = P/(Ce), e is Euler's number.

Productivity P is in terms of abundance per unit of N_1 and N_2.

The hockey stick is of the following form:

\textrm{Smolt} = \begin{cases} p N_1 &, N_1 \le \frac{N_1}{N_2} \times C\\ \frac{N_1}{N_2} \times C &, \textrm{otherwise} \end{cases}

Value

Numeric, the abundance of survivors

See Also

calc_SRRpars()

Examples

N1 <- 100
N2 <- 200
p <- 10
capacity <- 250

calc_SRR(N1, N2, p, capacity, type = "BH")

# Validation
SRRpars <- calc_SRRpars(p, capacity, type = "BH")
a <- SRRpars[1]
b <- SRRpars[2]
a * N1/(1 + b * N2)

Convert density-dependent survival parameters

Description

Converts from capacity/productivity parameters to alpha/beta stock-recruit parameters where productivity is in terms of smolts per spawner and alpha is terms of smolts per egg.

Usage

calc_SRRpars(p, capacity, f = 1, p_female = 1, type = c("BH", "Ricker", "HS"))

Arguments

Details

\alpha = \dfrac{P}{f \times p_{female}}

For the Beverton-Holt stock recruit relationship:

\beta = \dfrac{\alpha}{C}

For the Ricker stock recruit relationship:

\beta = \dfrac{\alpha}{Ce}

, e is Euler's number.

Value

Numeric vector length 2 for alpha and beta value, respectively

See Also

calc_SRR()

Examples

N1 <- 100
N2 <- 200
p <- 10
capacity <- 250

calc_SRRpars(p, capacity, type = "BH")

Ricker reference points

Description

Compute reference points (Umsy, Smsy, and Sgen) from Ricker stock-recruit function based on Scheuerell (2016).

Usage

calc_Smsy_Ricker(loga, b)

calc_Umsy_Ricker(loga)

calc_Sgen_Ricker(loga, b)

Arguments

Value

All three functions return a numeric

References

Scheuerell, M.D. 2016. An explicit solution for calculating optimum spawning stock size from Ricker’s stock recruitment model. PeerJ 4:e1623. doi:10.7717/peerj.1623

See Also

calc_ref()

Examples

a <- 3
Smax <- 100
b <- 1/Smax

calc_Smsy_Ricker(log(a), b)
calc_Umsy_Ricker(log(a))
calc_Sgen_Ricker(log(a), b)

Solve for broodtake numbers

Description

Internal functions to calculate broodtake with various constraints, called by brood_func(). calc_broodtake() is a wrapper function.

calc_broodtake_custom() uses a user-provided function to generate brood numbers and adjusts downwards if egg production exceeds the target.

.broodtake_func() is the optimization function used to calculate broodtake

Usage

calc_broodtake(
  NO,
  HO,
  stray,
  brood_import,
  ptarget_NOB,
  pmax_NOB,
  phatchery,
  egg_target,
  p_female,
  fec,
  s_prespawn,
  m
)

calc_broodtake_custom(
  f_brood,
  NO,
  HO,
  stray,
  p_female,
  fec,
  s_prespawn,
  m,
  egg_target
)

.broodtake_func(
  ptake_unmarked,
  NO,
  HO,
  stray,
  brood_import,
  phatchery,
  p_female,
  fec,
  egg_target,
  s_prespawn,
  ptarget_NOB,
  m = 1,
  opt = TRUE
)

Arguments

Value

calc_broodtake() returns a list from .broodtake_func()

calc_broodtake_custom() returns a named list, same format as calc_broodtake()

.broodtake_func() returns a numeric if opt = TRUE: log(p_unmarked) - log(ptarget_NOB), otherwise a named list of brood and egg production.

• egg_NOB Matrix ⁠[nage, n_g]⁠

• egg_HOB_unmarked Matrix ⁠[nage, n_r]⁠ (including strays)

• egg_HOB_marked Matrix ⁠[nage, n_r]⁠

• egg_HOB_import Vector ⁠[nage]⁠

• ptake_unmarked Numeric

• ptake_marked Numeric

• pNOB Numeric

• NOB Numeric

• HOB_unmarked Matrix ⁠[nage, n_r]⁠

• HOB_marked Matrix ⁠[nage, n_r]⁠

• HOB_import Matrix ⁠[nage, n_r]⁠

• HOB_stray Vector ⁠[nage]⁠

Calculate equilibrium quantities with life history groups

Description

Calculate eggs/smolt or spawners/smolt based on life history parameters (survival, maturity, fecundity)

Usage

calc_phi(
  Mjuv,
  p_mature,
  p_female,
  fec,
  s_enroute = 1,
  n_g = 1,
  p_LHG,
  output = c("egg", "spawner")
)

Arguments

Value

Numeric, units depend on "output" argument

Proportion wild spawners

Description

Calculate the proportion of wild spawners from a time series of spawners

• calc_pwild() is the simple calculation based on the proportion of hatchery spawners

• calc_pwild_age() performs the calculation weighted by age class fecundity

Usage

calc_pwild(pHOS_cur, pHOS_prev, gamma)

calc_pwild_age(NOS_a, HOS_a, fec, gamma)

Arguments

Value

calc_pwild_age() a matrix of pWILD by simulation and year. calc_pwild() returns a numeric

Examples

calc_pwild(0.9, 0.4, 0.8)

Reference points

Description

Calculate MSY and Sgen reference points for the operating model. Uses the biological parameters (maturity, natural mortality) in the last year of the projection.

• calc_MSY() calculates the MSY reference points from a set of biological and fishery parameters

• calc_Sgen() calculates the Sgen, the spawner abundance that would reach the spawner abundance at MSY after one generation without fishing

• calc_ref() is a wrapper function that calculates MSY and Sgen for an operating model

Usage

calc_ref(SOM, rel_F, check = TRUE, maximize = c("MSY", "MER"), AEQ = TRUE)

calc_MSY(
  Mjuv,
  fec,
  p_female,
  rel_F,
  vulPT,
  vulT,
  p_mature,
  s_enroute,
  n_g = 1,
  p_LHG = 1,
  SRRpars,
  maximize = c("MSY", "MER"),
  AEQ = TRUE,
  F_search = c(1e-08, 5)
)

calc_Sgen(
  Mjuv,
  fec,
  p_female,
  rel_F,
  vulPT,
  vulT,
  p_mature,
  s_enroute,
  n_g = 1,
  p_LHG = 1,
  SRRpars,
  SMSY,
  F_search = c(1e-08, 100),
  nyears
)

Arguments

Value

• calc_MSY returns a vector of various state variables (catch, exploitation rate, egg production, spawners) at MSY

• calc_Sgen returns a numeric

• calc_ref returns a list by stock, each containing a matrix of MSY state variables and Sgen by simulation

See Also

calc_Smsy_Ricker()

Examples

ref_simple <- calc_ref(simple_SOM)
ref_multi <- calc_ref(multi_SOM)

Smolt production

Description

Calculate smolt production from base stock-recruit parameters and fitness loss

Usage

calc_smolt(
  N1,
  N2 = N1,
  kappa,
  capacity,
  Smax,
  phi = 1,
  tau = 1,
  fitness_loss = 1,
  SRrel = c("BH", "Ricker"),
  per_recruit = FALSE
)

Arguments

Value

Numeric

Calculate spawners after broodtake

Description

Internal function that calculates remaining spawners after broodtake and additional removals.

Usage

calc_spawners(broodtake, NO, HO, stray, phatchery, premove_HOS, premove_NOS, m)

Arguments

Value

Named list:

• NOS ⁠[nage, n_g]⁠ natural-origin spawners

• HOS ⁠[nage, n_r]⁠ hatchery-origin spawners

• HOS_stray ⁠[nage, n_r]⁠ spawners that are strays

• NO_remove ⁠[nage, n_g]⁠ natural-origin fish removed before spawning

• HO_remove ⁠[nage, n_r]⁠ hatchery-origin fish removed before spawning

Calculate hatchery releases

Description

From egg target, calculate production of yearlings and subyearlings conditional on survival and proportion of releases.

.yearling_func() is the optimization function that determines proportions.

Usage

calc_yearling(egg_target, s_yearling, s_subyearling, p_yearling, p_subyearling)

.yearling_func(
  p_egg_yearling,
  egg_target,
  s_yearling,
  s_subyearling,
  p_yearling,
  opt = TRUE
)

Arguments

Value

calc_yearling() returns a named list returned by .yearling_func()

.yearling_func() returns a numeric if opt = TRUE: yearling/(yearling + subyearling) - p_yearling. Otherwise, log(p_unmarked) - log(ptarget_NOB)', otherwise a named list:

• yearling Vector ⁠[n_r]⁠ of yearling releases

• subyearling Vector ⁠[n_r]⁠ of subyearling releases

Catch function

Description

Internal function that does catch calculations for the preterminal and terminal marine fisheries.

Usage

catch_func(
  NO,
  HO,
  type = c("u", "catch"),
  U,
  K,
  V,
  MSF = FALSE,
  m = 1,
  release_mort = 0,
  p_mature_NO = array(1, dim(NO)),
  p_mature_HO = array(1, dim(HO)),
  AEQ_NO = array(1, dim(NO)),
  AEQ_HO = array(1, dim(HO))
)

Arguments

Value

Named list:

• K_NO kept natural-origin catch, same dimension as NO

• K_HO kept hatchery-origin catch, same dimension as HO

• D_NO discarded (live + dead) natural-origin catch, same dimension as NO

• D_HO discarded (live + dead) hatchery-origin catch, same dimension as HO

• DD_NO dead discarded natural-origin catch, same dimension as NO

• DD_HO dead discarded hatchery-origin catch, same dimension as HO

• Nsurv_NO natural-origin survivors, same dimension as NO

• Nsurv_HO hatchery-origin survivors, same dimension as HO

• U_NO natural-origin harvest rate (ratio of kept catch and abundance), vector ns

• U_HO hatchery-origin harvest rate (ratio of dead catch and abundance), vector ns

• Ex_NO natural-origin exploitation rate (ratio of dead catch and abundance), vector ns

• Ex_HO hatchery-origin exploitation rate (ratio of dead catch and abundance), vector ns

Check inputs to SOM object

Description

Ensures that the slots in the SOM object have the correct dimensions. Function will update some slots to their full dimensions.

Usage

check_SOM(SOM, silent = FALSE)

Arguments

Value

Updated SOM object with full dimensions in various slots

Examples

SOM_checked <- check_SOM(simple_SOM, silent = TRUE)

Compare scenarios in markdown

Description

Generate a markdown report for multiple model runs to compare scenarios. 3-5 is likely the ideal number of scenarios for comparison.

Usage

compare(
  SMSE_list,
  names,
  col_vec,
  filename = "SMSEcompare",
  dir = tempdir(),
  open_file = TRUE,
  render_args = list(),
  ...
)

Arguments

Value

Returns invisibly the output of rmarkdown::render(), typically the path of the output file

See Also

report()

Compare simulation runs

Description

Create figures that compare results across two dimensions

Usage

compare_spawners(SMSE_list, Design, prop = FALSE, FUN = median)

compare_fitness(SMSE_list, Design, FUN = median)

compare_escapement(SMSE_list, Design, FUN = median)

Arguments

Details

• compare_spawners() generates a time series of the composition of spawners

• compare_fitness() generates a time series of metrics (fitness, PNI, pHOS, and pWILD) related to hatchery production

• compare_escapement() generates a time series of the proportion of spawners and broodtake to escapement

Value

A ggplot object

Compare state variables from simulation runs

Description

Compare outputs from multiple simulations to evaluate performance across states of nature and/or management levers (identified by colour):

• compare_statevar_ts() produces a time series for all simulations, or with medians and 95th percentile intervals

• compare_statevar_hist() produces a histogram or density plot across all simulations for a particular year

Usage

compare_statevar_ts(
  SMSE_list,
  var = "PNI",
  s = 1,
  figure = TRUE,
  xlab = "Projection Year",
  quant = FALSE,
  ylab = var,
  ylim,
  agg.fun = sum,
  names,
  col_vec,
  ...
)

compare_statevar_hist(
  SMSE_list,
  var = "PNI",
  s = 1,
  y,
  figure = TRUE,
  xlab = var,
  names,
  col_vec,
  type = c("density", "hist"),
  ...
)

Arguments

Value

An array invisibly. Also generates base graphics if figure = TRUE

See Also

plot_statevar_ts() compare()

Create list of parameters

Description

Internal functions that convert the operating model inputs into a list or data frame of parameters to pass on to other internal functions.

Usage

define_hatchery_args(SOM)

define_habitat_args(SOM)

define_fitness_args(SOM)

define_SRRpars(SOM)

Arguments

Value

define_hatchery_args() returns a length nstocks of hatchery parameters, in-river removals, and behavior of hatchery-origin fish in the freshwater environment (fecundity, reproductive success, etc.)

define_habitat_args() returns a list of Habitat objects.

define_fitness_args() returns a length nstocks of fitness parameters.

define_SRRpars_args() returns a length nstocks, each of which is a data frame of stock-recruit parameters.

Fit conditioning model to historical data

Description

Bayesian stock reconstruction model of natural and hatchery origin fish population. Maturity and age-1 natural mortality are estimated from coded wire tag catch and escapement at age. A separate series of observed escapement, and hatchery releases reconstructs the population of interest, informed by natural mortality and maturity from CWT (Korman and Walters 2024). The model estimates time-varying maturity rate as well as time-varying ocean survival as a linear model of covariates (separate covariates for age 1 vs. ages 2+). The model can include either a preterminal juvenile fishery, terminal return fishery, or both (see Data and start sections of the documentation).

fit_CM() generates the RTMB model from data which can then be passed to sample_CM() to run the MCMC in Stan. Generate a markdown report with report_CM().

More information is available on the salmonMSE website

Usage

fit_CM(
  data,
  start = list(),
  map = list(),
  lower = list(),
  upper = list(),
  do_fit = TRUE,
  verbose = TRUE,
  silent = TRUE,
  control = list(eval.max = 1e+05, iter.max = 1e+05),
  ...
)

sample_CM(fit, ...)

Arguments

Value

• fit_CM() returns a named list containing the RTMB model (obj), nlminb output (opt), standard errors (SD), and parameter bounds (lower and upper)

• sample_CM() returns a stanfit object containing the MCMC chains

Data

Data should passed through a named list with the following entries.

• Nages Integer, number of age classes in the model

• Ldyr Integer, number of years in the model

• lht Integer, life history type. Should be 1 for now

• n_r Integer, number of release strategies for CWT, subset of a hatchery-origin brood year that differ in maturity rate. Default is 1.

• cwtrelease Matrix ⁠[Ldyr, n_r]⁠, coded wire tag (CWT) releases by release year and release strategy

• cwtesc Array ⁠[Ldyr, Nages, n_r]⁠. CWT escapement by release year, age, and release strategy (assumes broodyear is release year minus 1). Poisson likelihood.

• cwtcatPT Array ⁠[Ldyr, Nages, n_r]⁠. CWT preterminal catch (juvenile fish), by release year, age, and release strategy. Poisson likelihood. Set all values to zero to turn off parameters related to the preterminal fishery.

• cwtcatT Array ⁠[Ldyr, Nages, n_r]⁠. CWT terminal catch (returning, mature fish), by release year, age, and release strategy. Poisson likelihood. Set all values to zero to turn off parameters related to the terminal fishery.

• bvulPT Vector length Nages. Prior mean for the vulnerability at age to the preterminal fishery.

• bvulT Vector length Nages. Prior mean for the vulnerability at age to the terminal fishery.

• RelRegFPT Vector Ldyr. Trend in relative regional preterminal fishing mortality. Fishing mortality is estimated by estimating a scaling coefficient and annual deviations from this vector. Default is rep(1, d$Ldyr) (no prior trend) if cwtcatPT is provided, otherwise zero.

• RelRegFT Vector Ldyr. Trend in relative regional terminal fishing mortality. Default is rep(1, d$Ldyr) (no prior trend) if cwtcatT is provided, otherwise zero.

• bmatt Vector length Nages. Proportion maturity at age, base values for calculating the unfished replacement line. Also the prior means if year-specific maturity rates are estimated.

• mobase. Vector length Nages. Natural mortality at age, base values for calculating the unfished replacement line and the the equilibrium spawners at age.

• covariate1 Optional. Matrix ⁠Ldyr, ncov1⁠ of linear covariates that predict natural mortality for age 1.

• covariate Optional. Matrix ⁠Ldyr, ncov⁠ of linear covariates that predict natural mortality for ages 2+.

• hatchsurv Numeric, survival of hatchery releases into the smolt life stage. Density-independent. Default is 1. If less than 1, then hatchery origin fish have lower survival to age 2 (after first year of marine life stage) compared to natural origin fish.

• gamma Optional. Numeric, the relative spawning success of hatchery origin spawners. Default is 1.

• ssum Numeric, proportion of spawners that is female. Can also be a vector Nages

• fec Vector length Nages. Fecundity, egg production at age

• r_matt Integer, the release strategy for which to use maturity parameter for the natural system. Default is 1.

• obsescape Vector length Ldyr by return year, total observed escapement from fisheries, i.e., return to river (all ages and both hatchery/natural fish). Lognormal likelhood.

• propwildspawn Vector length Ldyr by return year, proportion of the escapement that spawn (accounts for en-route mortality and broodtake)

• hatchrelease Vector length Ldyr+1 by release year, number of hatchery releases for the population. Default is zero.

• obs_pHOS Optional. Vector length Ldyr by return year, observations of proportion of hatchery origin spawners (census) (between 0-1) by brood year. Fitted to model with logistic-normal likelihood.

• pHOS_sd Numeric, logistic-normal standard deviation of pHOS observations. Default is 1.

• pHOS_init Numeric, initial pHOS for equilibrium abundance in the first year of the model. Default is 0.

• s_enroute Numeric, survival of escapement to spawning grounds. Default is 1.

• so_mu Numeric, the prior mean for spawners at unfished replacement in logspace. Default is log(3 * max(data$obsescape)).

• so_sd Numeric, the prior standard deviation for spawners at unfished replacement in logspace. Default is 0.5.

• finitPT Numeric, initial preterminal fishing mortality for calculating the equilibrium juvenile proportions at age in the first year of the model. Default is 0. Set to "estimate" to allow the model to estimate the equilibrium condition.

• finitT Numeric, initial terminal fishing mortality for calculating the equilibrium juvenile proportions at age in the first year of the model. Default is 0. Set to "estimate" to allow the model to estimate the equilibrium condition.

• spawn_init Numeric, initial spawners to calculate equilibrium abundance in the first year of the model. Default is obsescape[1].

• cwtExp Numeric, the CWT expansion factor, typically the reciprocal of the catch sampling rate (higher factors for lower sampling rate). The model scales down the CWT predictions to match the observations. In other words, the model assumes that the CWT catch and escapement are not expanded. For example, cwtExp = 10 divides the CWT predictions by 10 for the likelihood. Default is 1. The Poisson distribution is used for the likelihood of the CWT observations, and the expansion parameter can be used to downweight the CWT likelihood relative to the escapement time series. However it requires adjustments of the CWT catches prior to fitting to ensure the proper population scale. If the expanded catch is 100, then the input CWT catch should be 10 and 50 with cwtExp of 10 and 2, respectively, to maintain the same population scale. The Poisson variance scales with the mean and is higher with cwtExp = 2.

• fitness Logical, whether to calculate fitness effects on survival. Default is FALSE.

• theta Vector length 2, the optimum phenotype value for the natural and hatchery environments. Default is 100 and 80, respectively. See online article for more information.

• rel_loss Vector length 3, the loss in fitness apportioned between the egg, fry (both prior to density-dependence), and smolt (after density-dependence) life stage. The three values should sum to 1.

• zbar_start Vector length 2, the mean phenotype of the spawners and broodtake in the natural and hatchery environment, respectively, at the start of the model. Default values of 100 and 100, implying maximum fitness at for the natural environment at the start of the model.

• fitness_variance Numeric. The variance (omega-squared) of the fitness function. Assumed identical between the natural and hatchery environments. Default is 100.

• phenotype_variance Numeric. The variance (sigma-squared) of the phenotypic trait (with optimum theta). Assumed identical between the natural and hatchery environments. Default is 10.

• heritability Numeric. The heritability (h-squared) of the phenotypic trait. Between 0-1. Default is 0.5.

• fitness_floor Numeric. The minimum fitness value in the natural and hatchery environments. Default is 0.5.

start

Starting values for parameters can be provided through a named list:

• log_cr Numeric, log of the compensation ratio (productivity). Default is 3.

• log_so Numeric, unfished spawners in logspace. Default is log(3 * max(data$obsescape)).

• moadd Numeric, additive term to base natural mortality rate for age 1 juveniles. Default is zero.

• wt Vector Ldyr. Annual deviates in natural mortality during the freshwater life stage (affects egg to smolt survival). Estimated with normal prior with mean zero and standard deviation p$wt_sd. Default is zero.

• wto Vector Ldyr. Annual deviates in natural mortality for age 1 juveniles (marine life stage). Estimated with normal prior with mean zero and standard deviation p$wto_sd. Default is zero.

• log_FbasePT Numeric, scaling coefficient to estimate preterminal fishing mortality from data$RelRegFPT. Default is log(0.1).

• log_FbaseT Numeric, scaling coefficient to estimate preterminal fishing mortality from data$RelRegFT. Default is log(0.1).

• log_fanomalyPT Vector Ldyr. Annual lognormal deviates from exp(log_FbasePT) * data$RelRegFPT to estimate preterminal fishing mortality. Estimated with normal prior with mean zero and standard deviation p$fanomaly_sd. Default is zero.

• log_fanomalyT Vector Ldyr. Annual lognormal deviates from exp(log_FbaseT) * data$RelRegFT to estimate terminal fishing mortality. Estimated with normal prior with mean zero and standard deviation p$fanomalyPT_sd. Default is zero.

• lnE_sd Numeric, lognormal standard deviation of the observed escapement. Estimated with hierarchical gamma(2, 5) prior. Default is 0.1.

• wt_sd Numeric, lognormal standard deviation of the egg to smolt (freshwater) natural mortality deviates. Estimated with hierarchical gamma(2, 5) prior. Default is 1.

• wto_sd Numeric, lognormal standard deviation of the age 1 (marine) natural mortality deviates. Estimated with hierarchical gamma(2, 5) prior. Default is 1.

• fanomalyPT_sd Numeric, lognormal standard deviation of fanomalyPT. Estimated with hierarchical gamma(2, 5) prior. Default is 1.

• fanomalyT_sd Numeric, lognormal standard deviation of fanomalyT. Estimated with hierarchical gamma(2, 5) prior. Default is 1.

• logit_vulPT Vector Nages-2 of preterminal vulnerability at age in logit space. Fixed to zero and one at age 1 and the maximum age, respectively. Default is qlogis(data$bvul_PT[-c(1, data$Nages)]).

• logit_vulT Vector Nages-2 of terminal vulnerability at age in logit space. Fixed to zero and one at age 1 and the maximum age, respectively. Default is qlogis(data$bvul_T[-c(1, data$Nages)]).

• logit_matt Matrix ⁠Ldyr, Nages-2⁠ maturity by year and age in logit space. Maturity is fixed to zero and one at age 1 and the maximum age, respectively. Default is matrix(qlogis(data$bmatt[-c(1, data$Nages)]), data$Ldyr, data$Nages-2, byrow = TRUE).

• sd_matt Vector Nages-2. Logit standard deviation of maturity (logit_matt) by age class. Default is 0.5.

• b1 Vector ncov1 of coefficients for linear covariates that predict natural mortality for age 1. Default is zero.

• b Vector ncov of coefficients for linear covariates that predict natural mortality for ages 2+. Default is zero.

Bounds

By default, the standard deviation parameters and parameters in normal space (e.g., FbasePT, Fbase_T) have a lower bound of zero. moadd has a lower bound of zero by default, but it is feasible that this parameter can be negative as well. Deviation parameters centred around zero are bounded between -3 to 3. The log_cr parameter has a lower bound of zero.

All other parameters are unbounded.

Covariates on natural mortality

Natural mortality is modeled as the sum of a base value M^\textrm{base}, additional scaling factor for age 1 M^\textrm{add}, a linear system of covariates X and coefficients b:

M_{y,a} = \begin{cases} M^\textrm{base}_a + M^\textrm{add} + \sum_j b^1_j X^1_{y,j} & \quad a = 1\\ M^\textrm{base}_a + \sum_j b_j X_{y,j} & \quad a = 2, \ldots, A \end{cases}

Author(s)

Q. Huynh from Stan code provided by J. Korman and C. Walters

References

Korman, J. and Walters, C. 2024. A life cycle model for Chinook salmon population dynamics. Canadian Contractor Report of Hydrography and Ocean Sciences 62: vi + 60 p.

See Also

report_CM()

CM2SOM()

Calculate mean phenotype and fitness

Description

Quantitative genetics model of mean phenotypic trait and fitness of next brood year.

calc_zbar() is the internal function that calculates mean phenotype.

Usage

fitness_func(
  Egg_NOS,
  Egg_HOS,
  Egg_NOB,
  Egg_HOB,
  zbar_brood,
  fitness_args = list()
)

calc_zbar(
  Egg_NOS,
  Egg_HOS,
  Egg_NOB,
  Egg_HOB,
  zbar_prev,
  fitness_variance,
  theta,
  phenotype_variance,
  heritability
)

Arguments

Value

fitness_func returns a list:

• zbar Numeric length 2, mean phenotype of next generation to natural zbar[1] and hatchery zbar[2] environments

• fitness Numeric length 2, fitness of next generation to natural and hatchery environments

• fitness_loss Matrix ⁠[2, 3]⁠ penalty in survival of next generation due to fitness effects

calc_zbar returns numeric length 2, mean phenotype of next generation

Calculate F from harvest rate

Description

Solves for apical instantaneous fishing mortality rate (F), proportional to fishing effort, from harvest rate (total retained catch over total abundance). The apical F can be greater than the realized F, if retention < 1. Used by AHA()

Usage

get_F(
  u = 0,
  K = 0,
  type = c("u", "catch"),
  M,
  N = 1,
  vul = 1,
  ret = 1,
  release_mort = 0,
  Fmax = 20
)

Arguments

Value

Numeric for the apical F

Figures for conditioning model results

Description

Functions used by the markdown report to generate summary figures from the age-structured conditoning model

Usage

get_report(stanfit, sims, inc_warmup = FALSE)

get_CMdata(fit)

CM_trace(stanfit, vars, inc_warmup = FALSE)

CM_pairs(stanfit, vars = c("log_so", "log_cr"), inc_warmup = FALSE)

CM_fit_esc(report, d, year)

CM_fit_pHOS(report, d, year)

CM_fit_CWTesc(report, d, year1 = 1, rs_names)

CM_fit_CWTcatch(report, d, PT = TRUE, year1 = 1, rs_names)

CM_maturity(
  report,
  d,
  year1 = 1,
  r = 1,
  brood = TRUE,
  annual = FALSE,
  rs_names
)

CM_vul(report, type = c("vulPT", "vulT"))

CM_SRR(report, year1 = 1)

CM_prod(report, d, year1 = 1, index = NULL, mean_bio = FALSE)

CM_Srep(
  report,
  d,
  year1 = 1,
  index = NULL,
  mean_bio = FALSE,
  type = c("spawner", "egg"),
  na.rm = FALSE
)

CM_M(report, year1 = 1, ci = TRUE)

CM_Megg(report, year1 = 1, ci = TRUE, surv = FALSE)

CM_Njuv(report, year1 = 1, ci = TRUE)

CM_recr(report, year1 = 1, ci = TRUE)

CM_esc(report, year1 = 1, ci = TRUE)

CM_F(report, PT = TRUE, year1 = 1, ci = TRUE)

CM_surv(report, year1 = 1, ci = TRUE)

CM_wt(stanfit, year1 = 1, ci = TRUE)

CM_surv2(report, year1 = 1, ci = TRUE, ylab = "Survival to age 2")

CM_wto(stanfit, year1 = 1, ci = TRUE)

CM_ER(
  report,
  brood = TRUE,
  type = c("PT", "T", "all"),
  year1 = 1,
  ci = TRUE,
  at_age = TRUE,
  r = 1,
  index_AEQ = NULL
)

CM_CWT_ER(
  report,
  brood = TRUE,
  type = c("PT", "T", "all"),
  year1 = 1,
  ci = TRUE,
  rs_names
)

CM_covariate(x, names, year1 = 1, b, ylab = "Covariate")

Arguments

Value

• get_report() returns the list of state variables by individual MCMC samples

• get_CMdata() returns the list of data variables used in the conditioning model

• CM_trace() returns a ggplot showing the MCMC trace plot (aka wormplot)

• CM_pairs() returns output from graphics::pairs(), a matrix of scatterplots of MCMC posterior samples

• CM_fit_esc() returns base graphics with fit to total escapement time series

• CM_fit_pHOS() returns base graphics with fit to pHOS (census) observations

• CM_fit_CWTesc() returns ggplot of fit to CWT escapement at age

• CM_fit_CWTcatch() returns ggplot of fit to CWT catch at age

• CM_maturity() returns ggplot of estimated maturity at age

• CM_vul() returns ggplot of estimated fishery vulnerability at age

• CM_SRR() returns ggplot of estimated stock-recruit relationship (density-dependent juvenile production from egg production) with average relationship and realized annual values. Years correspond to return years of egg production (predicts juvenile production of the following calendar year).

• CM_prod() returns ggplot of productivity (adults/spawner), calculated from density-dependent egg-smolt Ricker parameters, juvenile natural mortality, fecundity, and maturity. Even if egg-smolt survival function is constant, the realized productivity can vary with annual changes in natural mortality or maturity. .CM_prod() is the internal function that calculates productivity, with additional options for averaging time-varying biology.

• CM_Srep() returns ggplot of spawners or egg production at replacement, calculated from density-dependent egg-smolt Ricker parameters, juvenile natural mortality, fecundity, and maturity. Even if egg-smolt survival function is constant, the realized replacement can vary with annual changes in natural mortality or maturity. .CM_Srep() is the internal function with options for averaging time-varying biology.

• CM_M() returns ggplot of estimated natural mortality time series by age (marine stage)

• CM_Megg() returns ggplot of egg-juvenile mortality time series

• CM_Njuv() returns ggplot of juvenile abundance

• CM_recr() returns ggplot of recruitment (mature return)

• CM_esc() returns ggplot of escapement (after terminal harvest)

• CM_F() returns ggplot of instantaneous fishing mortality

• CM_surv() returns ggplot of natural survival (converting from instantaneous units of natural mortality)

• CM_wt() returns ggplot of annual deviations in egg-juvenile mortality from the Ricker function

• CM_surv2() returns ggplot of annual survival to age 2, which includes age-1 mortality (marine life stage) for both natural and hatchery origin fish. Hatchery fish experience additional mortality specified by release mortality.

• CM_wt() returns ggplot of annual deviations in age 1 natural mortality (first year in marine life stage, deviations from time series average)

• CM_ER() returns ggplot of exploitation rate either by individual age or aggregate values. Aggregate values use adult equivalents for preterminal fisheries. .CM_ER() and .CM_ERage() are the internal functions for reporting values for all simulations and years if desired.

• CM_CWT_ER() returns ggplot of CWT exploitation rate (by release strategy)

• CM_covariate() returns ggplot of mortality covariates

See Also

CM_MSY()

salmonMSE glossary

Description

Glossary of terms and parameters used in salmonMSE

Examples

data(glossary)
glossary[1:2, ]

Spawning and early life stage function

Description

Calculates egg production from spawners arriving at spawning grounds, broodtake and in-river removals from escapement of marine fisheries. This function also applies en-route mortality.

Usage

nextgen_SRR_func(
  Brood_Calcs,
  fec,
  p_female,
  hatchery_args = list(),
  fitness_args = list(),
  zbar_brood,
  SRRpars,
  p_LHG
)

nextgen_habitat_func(
  Brood_Calcs,
  Habitat,
  fec,
  p_female,
  hatchery_args = list(),
  fitness_args = list(),
  zbar_brood,
  p_LHG
)

Arguments

Value

Named list:

• NOS Matrix ⁠[nage, n_g]⁠

• HOS Matrix ⁠[nage, n_r]⁠

• HOS_effective Matrix ⁠[nage, n_r]⁠

• HOS_stray Matrix ⁠[nage, n_r]⁠

• pHOSeff Numeric

• pHOScensus Numeric

• Egg_NOS ⁠[nage, n_g]⁠

• Egg_HOS ⁠[nage, n_r]⁠

• Smolt_RelOut Vector ⁠[n_r]⁠

• Fry_NOS Vector ⁠[n_g]⁠

• Fry_HOS Vector ⁠[n_g]⁠

• Smolt_NOS Vector ⁠[n_g]⁠

• Smolt_HOS Vector ⁠[n_g]⁠

Plot life history groups and release strategies

Description

Plot the annual proportions of life history groups (natural origin fish) or release strategies (hatchery origin) at various life stages

Usage

plot_LHG(
  SMSE,
  var = "NOS",
  type = c("prop", "abs"),
  s = 1,
  FUN = median,
  figure = TRUE,
  xlab = "Projection Year",
  ylab,
  name,
  ylim
)

plot_RS(
  SMSE,
  var = "HOS",
  type = c("prop", "abs"),
  s = 1,
  FUN = median,
  figure = TRUE,
  xlab = "Projection Year",
  ylab,
  name,
  ylim
)

Arguments

Value

Base graphics figure, barplot of distribution or total numbers by LHG or RS. Returns invisibly the matrix of plotted values

See Also

plot_statevar_ts()

Decision table of performance metrics

Description

Generates a coloured table of a performance metric across two axes, which may be a population dynamics variable (e.g., productivity) or a management action (e.g., hatchery production levels or harvest strategy). See example at https://docs.salmonmse.com/articles/decision-table.html. More examples below.

• plot_decision_table() is a simple figure where colour range is intended to continuously transition from pink to white to green corresponding to values of 0, 0.5, and 1, respectively.

• plot_decision_table2() is converts performance metrics values into bins and provides more user control in the colour scheme

Usage

plot_decision_table(
  x,
  y,
  z,
  title,
  xlab,
  ylab,
  scenario,
  ncol = NULL,
  dir = "v"
)

plot_decision_table2(
  x,
  y,
  z,
  title,
  xlab,
  ylab,
  zlab,
  scenario,
  ncol = NULL,
  dir = "v",
  bin = c(0, 0.05, 0.25, 0.5, 0.75, 0.95),
  bin_labels = c("0-0.04", "0.05-0.24", "0.25-0.49", "0.5-0.74", "0.75-0.94", "0.95-1"),
  bin_col = c("purple4", "deeppink", "pink", "white", "green", "green4"),
  cell_border = FALSE,
  add_values = FALSE
)

Arguments

Value

ggplot object

See Also

plot_statevar_ts() plot_tradeoff()

Examples

# Simple decision table
results <- data.frame(
  PNI = c(0.7, 0.23, 0.05, 0.9, 0.85, 0.74, 0.95, 0.92, 0.9),
  pNOB = rep(c(0.5, 0.75, 1), each = 3),
  ER = rep(c(0.2, 0.3, 0.4), 3),
  scenario = "High productivity"
)
plot_decision_table(
  x = results$ER,
  y = results$pNOB,
  z = results$PNI,
  title = "PNI",
  xlab = "Exploitation rate",
  ylab = "pNOB target"
)

# Multiple decision tables organized by scenario
# Continuing from above
results_low <- results
results_low$scenario <- "Low productivity"
results_low$PNI <- 0.5 * results$PNI

results_all <- rbind(results, results_low)
plot_decision_table(
  x = results_all$ER,
  y = results_all$pNOB,
  z = results_all$PNI,
  title = "PNI",
  xlab = "Exploitation rate",
  ylab = "pNOB target",
  scenario = results_all$scenario
)
# Example of binned decision table

df <- expand.grid(
  SAR = seq(0.005, 0.03, 0.005),
  ER = seq(0, 0.5, 0.1)
)
df$value <- ifelse(5 * df$SAR + 0.2 > df$ER, 0.75, 0.05)
df$value <- ifelse(df$SAR < 0.02 & df$ER > 0.4, 0.04, df$value)

plot_decision_table2(
  x = df$SAR,
  y = df$ER,
  z = df$value,
  xlab = "Marine survival",
  ylab = "Exploitation rate",
  zlab = "Probability\nof objective"
)

Plot core output from salmonMSE

Description

Various functions that plot the state variables from salmonMSE projections:

• plot_statevar_ts() produces a time series for all simulations, or with medians and 95th percentile intervals

• plot_statevar_hist() produces a histogram across all simulations for a particular year

• plot_spawners() produces a summary barplot of spawners, including NOS, HOS, and wild spawners

• plot_escapement() produces a summary figure of the proportion of spawners and broodtake to escapement

• plot_fitness() produces a summary figure of metrics (fitness, PNI, pHOS, and pWILD) related to hatchery production

• plot_fishery() produces a summary figure of metrics related to the fishery, e.g., median catch, exploitation rate or harvest rate

Usage

plot_statevar_ts(
  SMSE,
  var = "PNI",
  s = 1,
  figure = TRUE,
  xlab = "Projection Year",
  quant = FALSE,
  ylab = var,
  ylim,
  agg.fun = sum,
  ...
)

plot_statevar_hist(SMSE, var = "PNI", s = 1, y, figure = TRUE, xlab = var, ...)

plot_spawners(SMSE, s = 1, prop = TRUE, FUN = median, figure = TRUE, ylim)

plot_fitness(SMSE, s = 1, FUN = median, figure = TRUE, ylim)

plot_escapement(SMSE, s = 1, FUN = median, figure = TRUE, ylim)

plot_fishery(
  SMSE,
  s = 1,
  type = c("catch", "exploit", "harvest"),
  FUN = median,
  figure = TRUE,
  ylim,
  ylab,
  ...
)

plot_Kobe(
  SMSE,
  s = 1,
  FUN = median,
  figure = TRUE,
  xlim,
  ylim,
  xlab = expression(NOS/S[MSY]),
  ylab = expression(U/U[MSY]),
  type = c("T", "PT")
)

Arguments

Value

Functions return the matrix of plotted values invisibly. Figure plotted from base graphics

See Also

plot_decision_table() plot_LHG() compare_statevar_ts()

Tradeoff figure

Description

Generates a tradeoff figure, a comparison between two performance metrics, across two variables which may represent a population dynamics variable (e.g., productivity) or a management action (e.g., hatchery production levels or harvest strategy). See example at https://docs.salmonmse.com/articles/decision-table.html

Usage

plot_tradeoff(
  pm1,
  pm2,
  x1,
  x2,
  xlab,
  ylab,
  x1lab,
  x2lab,
  scenario,
  scenario_rows,
  scenario_cols,
  ncol = NULL,
  dir = "v",
  add_letters = FALSE
)

Arguments

Value

ggplot object

See Also

plot_statevar_ts() plot_decision_table()

Examples

# Single tradeoff panel
results <- data.frame(
  PNI = c(0.7, 0.23, 0.05, 0.9, 0.85, 0.74, 0.95, 0.92, 0.9),
  Catch = c(10, 14, 12, 8, 7, 7, 8.2, 7.1, 6.9),
  pNOB = rep(c(0.5, 0.75, 1), each = 3),
  ER = rep(c(0.2, 0.3, 0.4), 3),
  scenario = "High productivity"
)

plot_tradeoff(
  pm1 = results$PNI,
  pm2 = results$Catch,
  x1 = results$ER,
  x2 = results$pNOB,
  xlab = "PNI",
  ylab = "Catch",
  x1lab = "Exploitation\nrate",
  x2lab = "pNOB\ntarget"
)

# Multiple panels, continuing from above code
results_low <- results
results_low$scenario <- "Low productivity"
results_low$PNI <- 0.5 * results$PNI
results_low$Catch <- c(0.9, 0.8, 0.7) * results$Catch

results_all <- rbind(results, results_low)
g <- plot_tradeoff(
  pm1 = results_all$PNI,
  pm2 = results_all$Catch,
  x1 = results_all$ER,
  x2 = results_all$pNOB,
  xlab = "PNI",
  ylab = "Catch",
  x1lab = "Exploitation\nrate",
  x2lab = "pNOB\ntarget",
  scenario = results_all$scenario
)

# Change legend with ggplot2
library(ggplot2)
g + scale_shape_manual(values = c(1, 4, 16))

Generate markdown reports

Description

Generate a markdown report for outcomes from a single operating model projection

Usage

## S4 method for signature 'SMSE'
report(
  object,
  name = object@Name,
  filename = "SMSE",
  dir = tempdir(),
  open_file = TRUE,
  render_args = list(),
  ...
)

Arguments

Value

Returns invisibly the output of rmarkdown::render(), typically the path of the output file

See Also

compare()

Conditioning model markdown report

Description

Generate a markdown report to plot time series and MCMC posteriors of estimates from the conditioning model. See get_report() for the various plotting functions used in the report.

Usage

report_CM(
  stanfit,
  year,
  cov1_names,
  cov_names,
  rs_names,
  name,
  filename = "CM",
  dir = tempdir(),
  open_file = TRUE,
  render_args = list(),
  ...
)

Arguments

Details

Report excludes MCMC values from warmup iterations

Value

Returns invisibly the output of rmarkdown::render(), typically the path of the output file

See Also

fit_CM() get_report()

Run salmonMSE

Description

salmonMSE() runs a salmon management strategy evaluation from an operating model object (SOM), by checking the operating model object with check_SOM(), running the projection in ProjectSOM() (parallel if called upon, then stitches together the output in a single object), and calculates reference points with calc_ref().

ProjectSOM() is the internal projection function.

simple_salmonMSE() is helper function that converts a simple operating model (modeling only recruit-spawners with Ricker relationship, terminal marine fishery, without hatchery production) to a full operating model and runs the projection.

Usage

salmonMSE(SOM, ncores = 1, silent = FALSE)

ProjectSOM(SOM, sims, check = FALSE)

simple_salmonMSE(simpleSOM, ...)

Arguments

Value

SMSE object

Examples

## Not run: 
SMSE <- salmonMSE(simple_SOM)

## End(Not run)

Environment to store salmon specific variables, e.g, fitness, during the simulation

Description

Environment to store salmon specific variables, e.g, fitness, during the simulation

Usage

salmonMSE_env

Format

An object of class environment of length 0.

Class "simpleSOM"

Description

A simple operating model for modeling recruit-spawners and a terminal marine fishery, no hatchery production.

Details

Various parameters can be stochastic (length nsim) or input as a single numeric (value identical across all simulations).

Slots

Name

Character. Identifying name

nsim

Integer. Number of simulations.

maxage

Integer. The maximum age of the population age structure.

ngen

Integer. The number of generations (life cycles) to run the projection.

kappa

Vector length nsim. The adult productivity ratio for the stock-recruit function. Units of recruits per spawner. Natural per-capita production of recruits as the population approaches zero (density-independent component).

Smax

Vector length nsim. The spawner abundance that maximizes smolt production in the Ricker stock-recruit function. Units of spawners.

sigmaR

Vector length nsim. The lognormal standard deviation in the Ricker stock-recruit relationship. Recruitment anomalies will be simulated with ⁠[stats::rlnorm()]⁠.

Recdev

Optional matrix ⁠[nsim, ngen-1]⁠ of recruitment anomalies in the Ricker stock-recruit relationship. Overrides the sigmaR argument and provides more user flexibility, for example, simulating with auto-correlation.

type_T

Character. Whether to manage terminal fishery catch from exploitation rate ("u") or catch target ("catch"). Default is "u".

u_terminal

Numeric, matrix ⁠[nsim, proyears]⁠, or function. If type_T = "u", the harvest rate (ratio of kept catch to of the terminal marine fishery. Function should be of the form function(NO, HO, m) return(u).

K_T

Numeric or function. If type_T = "catch", the catch target of the return in the terminal fishery. Function should be of the form function(NO, HO, m) return(K).

InitReturn

Single numeric or vector ⁠[nsim]⁠. The return at the beginning of the projection. Default assumes 1000.

Creating Object

Objects can be created by calls of the form new("simpleSOM")

Examples

showClass("simpleSOM")

Example objects from salmonMSE

Description

S4 objects containing operating model inputs to run the projection.

Usage

simple_SOM

multi_SOM

Format

An object of class SOM.

An object of class SOM of length 1.

Source

See salmonMSE website

Examples

data(simple_SOM)

data(multi_SOM)

Internal stray function

Description

Calculates the number of fish that move from donor to recipient populations from a movement matrix

Usage

stray_func(N, stray_matrix, m)

Arguments

Value

Named list:

• N_remain Abundance of hatchery-origin fish that do not stray, same dimension as N

• N_stray Abundance of strays by recipient population, same dimension as N

• m_stray Mark rate of strays by recipient population, vector ⁠[ns]⁠